In today's day and age, the vast majority of businesses retain extensive amounts of data regarding various aspects of their operations, such as inventories, customers, products, etc. Data about entities, such as people, products, or parts may be stored in digital format in a computer database. These computer databases permit the data about an entity to be accessed rapidly and permit the data to be cross-referenced to other relevant pieces of data about the same entity. The databases also permit a person to query the database to find data records pertaining to a particular entity. The terms data set, data file, and data source may also refer to a database. A database, however, has several limitations which may limit the ability of a person to find the correct data about an entity within the database. The actual data within the database is only as accurate as the person who entered the data. Thus, a mistake in the entry of the data into the database may cause a person looking for data about an entity in the database to miss some relevant data about the entity because, for example, a last name of a person was misspelled. Another kind of mistake involves creating a new separate record for an entity that already has a record within the database. In a third problem, several data records may contain information about the same entity, but, for example, the names or identification numbers contained in the two data records may be different so that the database may not be able to associate the two data records to each other.
For a business that operates one or more databases containing a large number of data records, the ability to locate relevant information about a particular entity within and among the respective databases is very important, but not easily obtained. Once again, any mistake in the entry of data (including without limitation the creation of more than one data record for the same entity) at any information source may cause relevant data to be missed when the data for a particular entity is searched for in the database. In addition, in cases involving multiple information sources, each of the information sources may have slightly different data syntax or formats which may further complicate the process of finding data among the databases. An example of the need to properly identify an entity referred to in a data record and to locate all data records relating to an entity in the health care field is one in which a number of different hospitals associated with a particular health care organization may have one or more information sources containing information about their patient, and a health care organization collects the information from each of the hospitals into a master database. It is necessary to link data records from all of the information sources pertaining to the same patient to enable searching for information for a particular patient in all of the hospital records.
There are several problems which limit the ability to find all of the relevant data about an entity in such a database. Multiple data records may exist for a particular entity as a result of separate data records received from one or more information sources, which leads to a problem that can be called data fragmentation. In the case of data fragmentation, a query of the master database may not retrieve all of the relevant information about a particular entity. In addition, as described above, the query may miss some relevant information about an entity due to a typographical error made during data entry, which leads to the problem of data inaccessibility. In addition, a large database may contain data records which appear to be identical, such as a plurality of records for people with the last name of Smith and the first name of Jim. A query of the database will retrieve all of these data records and a person who made the query to the database may often choose, at random, one of the data records retrieved which may be the wrong data record. The person may not often typically attempt to determine which of the records is appropriate. This can lead to the data records for the wrong entity being retrieved even when the correct data records are available. These problems limit the ability to locate the information for a particular entity within the database.
To reduce the amount of data that must be reviewed and to prevent the wrong data record from being picked, it is also desirable to identify and associate data records from the various information sources that may contain information about the same entity. There are conventional systems that locate duplicate data records within a database and delete those duplicate data records, but these systems only locate data records which are identical to each other. Thus, these conventional systems cannot determine for instance if two data records with slightly different last names nevertheless contain information about the same entity. For much the same reason, these conventional systems cannot associate data records with multiple entities.
Moreover, in some cases, it may be desirable to associate a data record for a person with other data records for that person to represent the entity of that person while at the same time it may be desired to associated the same data record for the person with other data records for others in the same household at the person to represent a household entity. Conventional systems which may only associate identical data records together do not have this desired capability.